Which of the following is a key assumption for the Independent T test?

The key assumption for the Independent T test is that the data is normally distributed within each group. This assumption is important because the Independent T test is designed to compare the means of two independent groups, and the accuracy of this test relies on the normality of the distribution of the data within each group being compared. If the data is normally distributed, it indicates that the majority of the scores are located around the mean, which allows for more reliable statistical inference regarding the comparison between the groups.

Normal distribution ensures that the parametric tests, like the Independent T test, yield valid results. When this assumption holds, the T test can provide meaningful insights into whether there are significant differences between the two groups' means. In cases where the assumption is violated, alternative methods, such as non-parametric tests, may be more appropriate.

The other aspects mentioned do not directly relate to the core assumptions needed for conducting an Independent T test, making them less relevant in this context. Specifically, repeated measures or comparisons involving only one group do not apply to this type of analysis, and the assumption is that variance should ideally be equal (homogeneity of variances) rather than unequal.